Pathological synchronization among spiking neurons in the basal ganglia-cortical loop within the brain is thought to be one factor contributing to the tremors exhibited by patients with Parkinson's disease. Deep Brain Stimulation (DBS), a well-established technique for mitigating these tremors, has been hypothesized to desynchronize these neurons through the injection of a high-frequency, pulsatile input into an appropriate region of the brain. Typically, DBS is implemented with a permanent, high frequency, pulsatile signal which is administered in an open-loop fashion. This has motivated researchers to search for alternative stimuli that consume less energy to prolong battery life and to mitigate side effects of DBS such as aggregate tissue damage. Control methods that employ feedback control are of particular interest because they can be used only when needed.
For example, double-pulse stimulation was shown to desynchronize a population of noisy phase oscillators and prevent resynchronization. Nonlinear, time-delayed feedback is used to experimentally desynchronize a system of electro-chemical oscillators. A minimum time desynchronizing control was used based on phase resetting for a coupled neural network was established using a Hamilton-Jacobi-Bellman approach, which was later extended to desynchronize neurons using an energy-optimal criterion. An energy-optimal, charge-balanced stimulus was used to control neural spike timing. More recently, a model was developed to control neural networks using a light sensitive protein, instead of electrical stimuli. The present invention advances the art of DBS by using chaotic desynchronization of neural populations.